Overview

Dataset statistics

Number of variables40
Number of observations3329147
Missing cells65665372
Missing cells (%)49.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1016.0 MiB
Average record size in memory320.0 B

Variable types

Categorical16
Numeric17
Unsupported7

Alerts

id_mutation has a high cardinality: 1432599 distinct values High cardinality
date_mutation has a high cardinality: 364 distinct values High cardinality
adresse_nom_voie has a high cardinality: 484404 distinct values High cardinality
adresse_code_voie has a high cardinality: 16111 distinct values High cardinality
nom_commune has a high cardinality: 30535 distinct values High cardinality
ancien_nom_commune has a high cardinality: 591 distinct values High cardinality
id_parcelle has a high cardinality: 2032421 distinct values High cardinality
ancien_id_parcelle has a high cardinality: 10339 distinct values High cardinality
code_nature_culture_speciale has a high cardinality: 125 distinct values High cardinality
nature_culture_speciale has a high cardinality: 125 distinct values High cardinality
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot5_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 4 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 3 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with lot5_surface_carrez and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot4_surface_carrez and 1 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 4 other fieldsHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot5_surface_carrezHigh correlation
nombre_pieces_principales is highly correlated with code_type_localHigh correlation
surface_terrain is highly correlated with lot1_surface_carrezHigh correlation
code_postal is highly correlated with ancien_code_communeHigh correlation
ancien_code_commune is highly correlated with code_postalHigh correlation
lot1_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with surface_reelle_bati and 1 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with surface_reelle_batiHigh correlation
lot4_surface_carrez is highly correlated with lot5_surface_carrezHigh correlation
lot5_surface_carrez is highly correlated with lot4_surface_carrezHigh correlation
code_type_local is highly correlated with nombre_pieces_principalesHigh correlation
surface_reelle_bati is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
nombre_pieces_principales is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_nature_culture is highly correlated with nature_cultureHigh correlation
nature_culture is highly correlated with code_nature_cultureHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
adresse_numero is highly correlated with adresse_suffixeHigh correlation
adresse_suffixe is highly correlated with adresse_numero and 2 other fieldsHigh correlation
code_postal is highly correlated with ancien_code_commune and 1 other fieldsHigh correlation
ancien_code_commune is highly correlated with adresse_suffixe and 1 other fieldsHigh correlation
lot1_surface_carrez is highly correlated with lot4_surface_carrez and 3 other fieldsHigh correlation
lot2_surface_carrez is highly correlated with lot3_surface_carrez and 2 other fieldsHigh correlation
lot3_surface_carrez is highly correlated with lot2_surface_carrez and 2 other fieldsHigh correlation
lot4_surface_carrez is highly correlated with adresse_suffixe and 4 other fieldsHigh correlation
lot5_surface_carrez is highly correlated with lot1_surface_carrez and 3 other fieldsHigh correlation
code_type_local is highly correlated with type_localHigh correlation
type_local is highly correlated with code_type_localHigh correlation
code_nature_culture is highly correlated with lot1_surface_carrez and 1 other fieldsHigh correlation
nature_culture is highly correlated with lot1_surface_carrez and 1 other fieldsHigh correlation
longitude is highly correlated with code_postal and 1 other fieldsHigh correlation
latitude is highly correlated with longitudeHigh correlation
adresse_numero has 1398919 (42.0%) missing values Missing
adresse_suffixe has 3184623 (95.7%) missing values Missing
ancien_code_commune has 3275534 (98.4%) missing values Missing
ancien_nom_commune has 3275534 (98.4%) missing values Missing
ancien_id_parcelle has 3315540 (99.6%) missing values Missing
numero_volume has 3319162 (99.7%) missing values Missing
lot1_numero has 2294729 (68.9%) missing values Missing
lot1_surface_carrez has 3039921 (91.3%) missing values Missing
lot2_numero has 3111925 (93.5%) missing values Missing
lot2_surface_carrez has 3258320 (97.9%) missing values Missing
lot3_numero has 3292644 (98.9%) missing values Missing
lot3_surface_carrez has 3322123 (99.8%) missing values Missing
lot4_numero has 3316424 (99.6%) missing values Missing
lot4_surface_carrez has 3327276 (99.9%) missing values Missing
lot5_numero has 3323135 (99.8%) missing values Missing
lot5_surface_carrez has 3328380 (> 99.9%) missing values Missing
code_type_local has 1512780 (45.4%) missing values Missing
type_local has 1512780 (45.4%) missing values Missing
surface_reelle_bati has 1970649 (59.2%) missing values Missing
nombre_pieces_principales has 1515599 (45.5%) missing values Missing
code_nature_culture has 1050173 (31.5%) missing values Missing
nature_culture has 1050173 (31.5%) missing values Missing
code_nature_culture_speciale has 3175092 (95.4%) missing values Missing
nature_culture_speciale has 3175092 (95.4%) missing values Missing
surface_terrain has 1050235 (31.5%) missing values Missing
longitude has 72660 (2.2%) missing values Missing
latitude has 72660 (2.2%) missing values Missing
numero_disposition is highly skewed (γ1 = 42.34108297) Skewed
valeur_fonciere is highly skewed (γ1 = 85.45130559) Skewed
lot1_surface_carrez is highly skewed (γ1 = 42.78337816) Skewed
lot2_surface_carrez is highly skewed (γ1 = 62.02338371) Skewed
lot3_surface_carrez is highly skewed (γ1 = 20.28587031) Skewed
lot4_numero is highly skewed (γ1 = 58.24379501) Skewed
nombre_lots is highly skewed (γ1 = 46.98893099) Skewed
surface_reelle_bati is highly skewed (γ1 = 134.4592681) Skewed
surface_terrain is highly skewed (γ1 = 82.35419667) Skewed
code_commune is an unsupported type, check if it needs cleaning or further analysis Unsupported
code_departement is an unsupported type, check if it needs cleaning or further analysis Unsupported
numero_volume is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot1_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot2_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot3_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
lot5_numero is an unsupported type, check if it needs cleaning or further analysis Unsupported
nombre_lots has 2294729 (68.9%) zeros Zeros
nombre_pieces_principales has 581298 (17.5%) zeros Zeros

Reproduction

Analysis started2021-10-05 23:06:37.958047
Analysis finished2021-10-05 23:22:15.054046
Duration15 minutes and 37.1 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id_mutation
Categorical

HIGH CARDINALITY

Distinct1432599
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
2018-1305371
 
3844
2018-20073
 
2097
2018-1361763
 
2092
2018-413181
 
1964
2018-1103729
 
1489
Other values (1432594)
3317661 

Length

Max length12
Median length11
Mean length11.19680357
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique690859 ?
Unique (%)20.8%

Sample

1st row2018-1
2nd row2018-1
3rd row2018-2
4th row2018-2
5th row2018-2

Common Values

ValueCountFrequency (%)
2018-13053713844
 
0.1%
2018-200732097
 
0.1%
2018-13617632092
 
0.1%
2018-4131811964
 
0.1%
2018-11037291489
 
< 0.1%
2018-13710001221
 
< 0.1%
2018-5869091093
 
< 0.1%
2018-5867451088
 
< 0.1%
2018-8053821039
 
< 0.1%
2018-12623631010
 
< 0.1%
Other values (1432589)3312210
99.5%

Length

2021-10-06T01:22:15.267097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-13053713844
 
0.1%
2018-200732097
 
0.1%
2018-13617632092
 
0.1%
2018-4131811964
 
0.1%
2018-11037291489
 
< 0.1%
2018-13710001221
 
< 0.1%
2018-5869091093
 
< 0.1%
2018-5867451088
 
< 0.1%
2018-8053821039
 
< 0.1%
2018-12623631010
 
< 0.1%
Other values (1432589)3312210
99.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date_mutation
Categorical

HIGH CARDINALITY

Distinct364
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
2018-12-21
 
42118
2018-12-28
 
38398
2018-12-20
 
31809
2018-12-27
 
30771
2018-06-29
 
30577
Other values (359)
3155474 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2018-01-03
2nd row2018-01-03
3rd row2018-01-04
4th row2018-01-04
5th row2018-01-04

Common Values

ValueCountFrequency (%)
2018-12-2142118
 
1.3%
2018-12-2838398
 
1.2%
2018-12-2031809
 
1.0%
2018-12-2730771
 
0.9%
2018-06-2930577
 
0.9%
2018-12-1425708
 
0.8%
2018-09-2824835
 
0.7%
2018-11-3024810
 
0.7%
2018-04-2723991
 
0.7%
2018-12-1923450
 
0.7%
Other values (354)3032680
91.1%

Length

2021-10-06T01:22:15.532711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-12-2142118
 
1.3%
2018-12-2838398
 
1.2%
2018-12-2031809
 
1.0%
2018-12-2730771
 
0.9%
2018-06-2930577
 
0.9%
2018-12-1425708
 
0.8%
2018-09-2824835
 
0.7%
2018-11-3024810
 
0.7%
2018-04-2723991
 
0.7%
2018-12-1923450
 
0.7%
Other values (354)3032680
91.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_disposition
Real number (ℝ≥0)

SKEWED

Distinct362
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.22082173
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:15.802772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum362
Range361
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.983262757
Coefficient of variation (CV)4.081892249
Kurtosis1994.248059
Mean1.22082173
Median Absolute Deviation (MAD)0
Skewness42.34108297
Sum4064295
Variance24.8329077
MonotonicityNot monotonic
2021-10-06T01:22:16.129358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13115008
93.6%
2175601
 
5.3%
324859
 
0.7%
44792
 
0.1%
52214
 
0.1%
6684
 
< 0.1%
7419
 
< 0.1%
8414
 
< 0.1%
9222
 
< 0.1%
12182
 
< 0.1%
Other values (352)4752
 
0.1%
ValueCountFrequency (%)
13115008
93.6%
2175601
 
5.3%
324859
 
0.7%
44792
 
0.1%
52214
 
0.1%
6684
 
< 0.1%
7419
 
< 0.1%
8414
 
< 0.1%
9222
 
< 0.1%
10169
 
< 0.1%
ValueCountFrequency (%)
3621
< 0.1%
3611
< 0.1%
3601
< 0.1%
3592
< 0.1%
3581
< 0.1%
3571
< 0.1%
3561
< 0.1%
3551
< 0.1%
3541
< 0.1%
3531
< 0.1%

nature_mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
Vente
2996397 
Vente en l'état futur d'achèvement
 
256130
Echange
 
47457
Vente terrain à bâtir
 
13758
Adjudication
 
12900

Length

Max length34
Median length5
Mean length7.358908153
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVente
2nd rowVente
3rd rowVente
4th rowVente
5th rowVente

Common Values

ValueCountFrequency (%)
Vente2996397
90.0%
Vente en l'état futur d'achèvement256130
 
7.7%
Echange47457
 
1.4%
Vente terrain à bâtir13758
 
0.4%
Adjudication12900
 
0.4%
Expropriation2505
 
0.1%

Length

2021-10-06T01:22:16.492121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:22:16.686165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
vente3266285
74.3%
d'achèvement256130
 
5.8%
futur256130
 
5.8%
l'état256130
 
5.8%
en256130
 
5.8%
echange47457
 
1.1%
bâtir13758
 
0.3%
à13758
 
0.3%
terrain13758
 
0.3%
adjudication12900
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

valeur_fonciere
Real number (ℝ≥0)

SKEWED

Distinct132875
Distinct (%)4.0%
Missing31915
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean800188.1149
Minimum0.13
Maximum1256965630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:16.985577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile2370
Q156000
median143700
Q3260000
95-th percentile1245000
Maximum1256965630
Range1256965630
Interquartile range (IQR)204000

Descriptive statistics

Standard deviation12078005.65
Coefficient of variation (CV)15.09395781
Kurtosis8596.509286
Mean800188.1149
Median Absolute Deviation (MAD)96688
Skewness85.45130559
Sum2.638405859 × 1012
Variance1.458782204 × 1014
MonotonicityNot monotonic
2021-10-06T01:22:17.762751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000028692
 
0.9%
15000027861
 
0.8%
127224
 
0.8%
12000027110
 
0.8%
8000024033
 
0.7%
5000023887
 
0.7%
11000022895
 
0.7%
9000022884
 
0.7%
20000022804
 
0.7%
13000022384
 
0.7%
Other values (132865)3047458
91.5%
(Missing)31915
 
1.0%
ValueCountFrequency (%)
0.132
 
< 0.1%
0.15118
< 0.1%
0.165
 
< 0.1%
0.172
 
< 0.1%
0.1850
< 0.1%
0.191
 
< 0.1%
0.22
 
< 0.1%
0.252
 
< 0.1%
0.272
 
< 0.1%
0.31
 
< 0.1%
ValueCountFrequency (%)
125696563040
 
< 0.1%
1249132030205
< 0.1%
6295914203
 
< 0.1%
5980157401
 
< 0.1%
47709408012
 
< 0.1%
45886576047
 
< 0.1%
42100000022
 
< 0.1%
3626937281
 
< 0.1%
31868880034
 
< 0.1%
31060000079
 
< 0.1%

adresse_numero
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct7201
Distinct (%)0.4%
Missing1398919
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean788.0530373
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:18.099828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median26
Q3102
95-th percentile5754
Maximum9999
Range9998
Interquartile range (IQR)94

Descriptive statistics

Standard deviation2132.927192
Coefficient of variation (CV)2.706578227
Kurtosis7.165713917
Mean788.0530373
Median Absolute Deviation (MAD)22
Skewness2.885265279
Sum1521122038
Variance4549378.407
MonotonicityNot monotonic
2021-10-06T01:22:18.413899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181857
 
2.5%
276666
 
2.3%
361201
 
1.8%
459856
 
1.8%
556079
 
1.7%
653685
 
1.6%
747644
 
1.4%
847113
 
1.4%
1043480
 
1.3%
940927
 
1.2%
Other values (7191)1361720
40.9%
(Missing)1398919
42.0%
ValueCountFrequency (%)
181857
2.5%
276666
2.3%
361201
1.8%
459856
1.8%
556079
1.7%
653685
1.6%
747644
1.4%
847113
1.4%
940927
1.2%
1043480
1.3%
ValueCountFrequency (%)
9999345
< 0.1%
999855
 
< 0.1%
999724
 
< 0.1%
999610
 
< 0.1%
999512
 
< 0.1%
999422
 
< 0.1%
99932
 
< 0.1%
99922
 
< 0.1%
999115
 
< 0.1%
999024
 
< 0.1%

adresse_suffixe
Categorical

HIGH CORRELATION
MISSING

Distinct41
Distinct (%)< 0.1%
Missing3184623
Missing (%)95.7%
Memory size25.4 MiB
B
83313 
A
22573 
F
14094 
T
11309 
C
 
4497
Other values (36)
8738 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowZ
2nd rowZ
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B83313
 
2.5%
A22573
 
0.7%
F14094
 
0.4%
T11309
 
0.3%
C4497
 
0.1%
D2167
 
0.1%
E1201
 
< 0.1%
Q1060
 
< 0.1%
P754
 
< 0.1%
G557
 
< 0.1%
Other values (31)2999
 
0.1%
(Missing)3184623
95.7%

Length

2021-10-06T01:22:18.722969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b83313
57.6%
a22573
 
15.6%
f14094
 
9.8%
t11309
 
7.8%
c4497
 
3.1%
d2167
 
1.5%
e1201
 
0.8%
q1060
 
0.7%
p754
 
0.5%
g557
 
0.4%
Other values (27)2999
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_nom_voie
Categorical

HIGH CARDINALITY

Distinct484404
Distinct (%)14.7%
Missing30430
Missing (%)0.9%
Memory size25.4 MiB
LE VILLAGE
 
31345
LE BOURG
 
26496
RUE JEAN JAURES
 
6750
GR GRANDE RUE
 
6140
AV JEAN JAURES
 
6096
Other values (484399)
3221890 

Length

Max length31
Median length14
Mean length14.65257462
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165688 ?
Unique (%)5.0%

Sample

1st rowRUE GEN LOGEROT
2nd rowRUE GEN LOGEROT
3rd rowRUE DE LA BARMETTE
4th rowRUE DE LA BARMETTE
5th rowRUE DE LA BARMETTE

Common Values

ValueCountFrequency (%)
LE VILLAGE31345
 
0.9%
LE BOURG26496
 
0.8%
RUE JEAN JAURES6750
 
0.2%
GR GRANDE RUE6140
 
0.2%
AV JEAN JAURES6096
 
0.2%
RUE DE LA REPUBLIQUE5877
 
0.2%
RUE PASTEUR5248
 
0.2%
AV DE LA REPUBLIQUE5008
 
0.2%
RUE VICTOR HUGO4971
 
0.1%
RUE DE PARIS4217
 
0.1%
Other values (484394)3196569
96.0%
(Missing)30430
 
0.9%

Length

2021-10-06T01:22:19.099069image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rue1086845
 
11.5%
de715473
 
7.6%
la488261
 
5.2%
du329727
 
3.5%
le288874
 
3.1%
des278546
 
2.9%
av254732
 
2.7%
les230910
 
2.4%
che104599
 
1.1%
rte99464
 
1.1%
Other values (202213)5571404
59.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

adresse_code_voie
Categorical

HIGH CARDINALITY

Distinct16111
Distinct (%)0.5%
Missing30389
Missing (%)0.9%
Memory size25.4 MiB
B005
 
15659
B004
 
15081
B009
 
15002
B002
 
14654
B003
 
14515
Other values (16106)
3223847 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1383 ?
Unique (%)< 0.1%

Sample

1st row1660
2nd row1660
3rd row0025
4th row0025
5th row0025

Common Values

ValueCountFrequency (%)
B00515659
 
0.5%
B00415081
 
0.5%
B00915002
 
0.5%
B00214654
 
0.4%
B00314515
 
0.4%
B01114508
 
0.4%
B00814472
 
0.4%
B01414354
 
0.4%
B01314298
 
0.4%
B00614271
 
0.4%
Other values (16101)3151944
94.7%
(Missing)30389
 
0.9%

Length

2021-10-06T01:22:19.387140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b00515659
 
0.5%
b00415081
 
0.5%
b00915002
 
0.5%
b00214654
 
0.4%
b00314515
 
0.4%
b01114508
 
0.4%
b00814472
 
0.4%
b01414354
 
0.4%
b01314298
 
0.4%
b00614271
 
0.4%
Other values (16101)3151944
95.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_postal
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5865
Distinct (%)0.2%
Missing30556
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean50433.12658
Minimum1000
Maximum97490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:19.641205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile6600
Q129300
median49240
Q375014
95-th percentile93160
Maximum97490
Range96490
Interquartile range (IQR)45714

Descriptive statistics

Standard deviation27431.69274
Coefficient of variation (CV)0.5439221123
Kurtosis-1.196371386
Mean50433.12658
Median Absolute Deviation (MAD)23940
Skewness-0.004125740497
Sum1.663582575 × 1011
Variance752497766.8
MonotonicityNot monotonic
2021-10-06T01:22:19.948305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
750158066
 
0.2%
691007890
 
0.2%
210007417
 
0.2%
312007403
 
0.2%
350007308
 
0.2%
540006509
 
0.2%
511006480
 
0.2%
750166477
 
0.2%
750186232
 
0.2%
140006077
 
0.2%
Other values (5855)3228732
97.0%
(Missing)30556
 
0.9%
ValueCountFrequency (%)
10001743
0.1%
1090347
 
< 0.1%
11001241
< 0.1%
1110608
 
< 0.1%
1120650
 
< 0.1%
1130356
 
< 0.1%
1140593
 
< 0.1%
11501118
< 0.1%
1160717
 
< 0.1%
11701809
0.1%
ValueCountFrequency (%)
974901649
< 0.1%
97480594
 
< 0.1%
97470304
 
< 0.1%
97460630
 
< 0.1%
97450187
 
< 0.1%
97442117
 
< 0.1%
97441257
 
< 0.1%
97440703
< 0.1%
97439114
 
< 0.1%
97438955
< 0.1%

code_commune
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size25.4 MiB

nom_commune
Categorical

HIGH CARDINALITY

Distinct30535
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
Toulouse
 
27033
Nice
 
16665
Nantes
 
16149
Montpellier
 
15364
Bordeaux
 
14567
Other values (30530)
3239369 

Length

Max length45
Median length10
Mean length11.88880395
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)< 0.1%

Sample

1st rowBourg-en-Bresse
2nd rowBourg-en-Bresse
3rd rowNivigne et Suran
4th rowNivigne et Suran
5th rowNivigne et Suran

Common Values

ValueCountFrequency (%)
Toulouse27033
 
0.8%
Nice16665
 
0.5%
Nantes16149
 
0.5%
Montpellier15364
 
0.5%
Bordeaux14567
 
0.4%
Lille12736
 
0.4%
Rennes12102
 
0.4%
Saint-Étienne8794
 
0.3%
Paris 15e Arrondissement8152
 
0.2%
Villeurbanne8023
 
0.2%
Other values (30525)3189562
95.8%

Length

2021-10-06T01:22:20.321405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arrondissement121016
 
3.1%
la101164
 
2.6%
le95338
 
2.5%
paris68131
 
1.8%
les34551
 
0.9%
marseille31056
 
0.8%
toulouse27033
 
0.7%
lyon21829
 
0.6%
nice16665
 
0.4%
nantes16149
 
0.4%
Other values (30440)3332359
86.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_departement
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size25.4 MiB

ancien_code_commune
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct592
Distinct (%)1.1%
Missing3275534
Missing (%)98.4%
Infinite0
Infinite (%)0.0%
Mean57625.90385
Minimum1033
Maximum95306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:20.620062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1033
5-th percentile11251
Q135184
median61228
Q385048
95-th percentile93070
Maximum95306
Range94273
Interquartile range (IQR)49864

Descriptive statistics

Standard deviation27652.05033
Coefficient of variation (CV)0.4798545183
Kurtosis-1.133457986
Mean57625.90385
Median Absolute Deviation (MAD)23966
Skewness-0.401882782
Sum3089497583
Variance764635887.7
MonotonicityNot monotonic
2021-10-06T01:22:20.911127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
851943433
 
0.1%
912282148
 
0.1%
930701904
 
0.1%
785511488
 
< 0.1%
73257860
 
< 0.1%
95306834
 
< 0.1%
91182830
 
< 0.1%
85146769
 
< 0.1%
16192716
 
< 0.1%
22093678
 
< 0.1%
Other values (582)39953
 
1.2%
(Missing)3275534
98.4%
ValueCountFrequency (%)
1033481
< 0.1%
103668
 
< 0.1%
10595
 
< 0.1%
1091276
< 0.1%
109722
 
< 0.1%
112262
 
< 0.1%
113066
 
< 0.1%
115422
 
< 0.1%
1185278
< 0.1%
118619
 
< 0.1%
ValueCountFrequency (%)
95306834
 
< 0.1%
930701904
0.1%
91390166
 
< 0.1%
912282148
0.1%
912224
 
< 0.1%
91182830
 
< 0.1%
9007348
 
< 0.1%
9006827
 
< 0.1%
8944810
 
< 0.1%
894218
 
< 0.1%

ancien_nom_commune
Categorical

HIGH CARDINALITY
MISSING

Distinct591
Distinct (%)1.1%
Missing3275534
Missing (%)98.4%
Memory size25.4 MiB
Les Sables-d'Olonne
 
3433
Évry
 
2148
Saint-Ouen
 
1904
Saint-Germain-en-Laye
 
1488
Les Belleville
 
860
Other values (586)
43780 

Length

Max length30
Median length11
Mean length12.84334023
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowCras-sur-Reyssouze
2nd rowCras-sur-Reyssouze
3rd rowCras-sur-Reyssouze
4th rowCras-sur-Reyssouze
5th rowCras-sur-Reyssouze

Common Values

ValueCountFrequency (%)
Les Sables-d'Olonne3433
 
0.1%
Évry2148
 
0.1%
Saint-Ouen1904
 
0.1%
Saint-Germain-en-Laye1488
 
< 0.1%
Les Belleville860
 
< 0.1%
Herblay834
 
< 0.1%
Courcouronnes830
 
< 0.1%
Montaigu769
 
< 0.1%
Roumazières-Loubert716
 
< 0.1%
Lamballe678
 
< 0.1%
Other values (581)39953
 
1.2%
(Missing)3275534
98.4%

Length

2021-10-06T01:22:21.241308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
les5928
 
9.0%
sables-d'olonne3433
 
5.2%
évry2148
 
3.3%
saint-ouen1904
 
2.9%
le1576
 
2.4%
saint-germain-en-laye1488
 
2.3%
la1486
 
2.3%
belleville1455
 
2.2%
herblay834
 
1.3%
courcouronnes830
 
1.3%
Other values (599)44855
68.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

id_parcelle
Categorical

HIGH CARDINALITY

Distinct2032421
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Memory size25.4 MiB
31462000AL0070
 
1158
91570000AN0126
 
1051
95280000AB0305
 
1004
95018000BP0350
 
834
930630000T0252
 
796
Other values (2032416)
3324304 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1621119 ?
Unique (%)48.7%

Sample

1st row01053000AN0073
2nd row01053000AN0073
3rd row01095000AH0186
4th row01095000AH0186
5th row01095000AH0186

Common Values

ValueCountFrequency (%)
31462000AL00701158
 
< 0.1%
91570000AN01261051
 
< 0.1%
95280000AB03051004
 
< 0.1%
95018000BP0350834
 
< 0.1%
930630000T0252796
 
< 0.1%
78586000AE0360763
 
< 0.1%
33333000AR0002714
 
< 0.1%
930100000P0163677
 
< 0.1%
47001000AR0685674
 
< 0.1%
42218000IN0024652
 
< 0.1%
Other values (2032411)3320824
99.7%

Length

2021-10-06T01:22:21.640220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
31462000al00701158
 
< 0.1%
91570000an01261051
 
< 0.1%
95280000ab03051004
 
< 0.1%
95018000bp0350834
 
< 0.1%
930630000t0252796
 
< 0.1%
78586000ae0360763
 
< 0.1%
33333000ar0002714
 
< 0.1%
930100000p0163677
 
< 0.1%
47001000ar0685674
 
< 0.1%
42218000in0024652
 
< 0.1%
Other values (2032411)3320824
99.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ancien_id_parcelle
Categorical

HIGH CARDINALITY
MISSING

Distinct10339
Distinct (%)76.0%
Missing3315540
Missing (%)99.6%
Memory size25.4 MiB
78524000AD0005
 
488
91182000AP0062
 
165
91182000AP0061
 
154
91182000AN0020
 
88
91182000AB0141
 
62
Other values (10334)
12650 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8924 ?
Unique (%)65.6%

Sample

1st row011540000A0391
2nd row01154000ZC0027
3rd row01154000AA0344
4th row01154000AA0346
5th row01154000AA0348

Common Values

ValueCountFrequency (%)
78524000AD0005488
 
< 0.1%
91182000AP0062165
 
< 0.1%
91182000AP0061154
 
< 0.1%
91182000AN002088
 
< 0.1%
91182000AB014162
 
< 0.1%
782510000B025648
 
< 0.1%
78524000AB010134
 
< 0.1%
91182000AP008931
 
< 0.1%
91182000AN052830
 
< 0.1%
91182000AN000724
 
< 0.1%
Other values (10329)12483
 
0.4%
(Missing)3315540
99.6%

Length

2021-10-06T01:22:21.899560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
78524000ad0005488
 
3.6%
91182000ap0062165
 
1.2%
91182000ap0061154
 
1.1%
91182000an002088
 
0.6%
91182000ab014162
 
0.5%
782510000b025648
 
0.4%
78524000ab010134
 
0.2%
91182000ap008931
 
0.2%
91182000an052830
 
0.2%
85166000ax009924
 
0.2%
Other values (10329)12483
91.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numero_volume
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3319162
Missing (%)99.7%
Memory size25.4 MiB

lot1_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing2294729
Missing (%)68.9%
Memory size25.4 MiB

lot1_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct18727
Distinct (%)6.5%
Missing3039921
Missing (%)91.3%
Infinite0
Infinite (%)0.0%
Mean63.34290009
Minimum0.1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:22.153698image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile16.06
Q133.6125
median53.39
Q373.63
95-th percentile118.81
Maximum9999
Range9998.9
Interquartile range (IQR)40.0175

Descriptive statistics

Standard deviation133.178849
Coefficient of variation (CV)2.10250634
Kurtosis2506.866421
Mean63.34290009
Median Absolute Deviation (MAD)19.98
Skewness42.78337816
Sum18320413.62
Variance17736.60583
MonotonicityNot monotonic
2021-10-06T01:22:22.449635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.5950
 
< 0.1%
12667
 
< 0.1%
15498
 
< 0.1%
13426
 
< 0.1%
10377
 
< 0.1%
65360
 
< 0.1%
60353
 
< 0.1%
40349
 
< 0.1%
14345
 
< 0.1%
20335
 
< 0.1%
Other values (18717)284566
 
8.5%
(Missing)3039921
91.3%
ValueCountFrequency (%)
0.11
< 0.1%
0.281
< 0.1%
0.361
< 0.1%
0.511
< 0.1%
0.551
< 0.1%
0.61
< 0.1%
0.651
< 0.1%
0.71
< 0.1%
0.731
< 0.1%
0.82
< 0.1%
ValueCountFrequency (%)
999912
< 0.1%
99011
 
< 0.1%
96541
 
< 0.1%
9461.51
 
< 0.1%
94271
 
< 0.1%
9269.21
 
< 0.1%
78005
< 0.1%
74181
 
< 0.1%
72571
 
< 0.1%
71191
 
< 0.1%

lot2_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3111925
Missing (%)93.5%
Memory size25.4 MiB

lot2_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct12460
Distinct (%)17.6%
Missing3258320
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean64.16103167
Minimum0.1
Maximum8284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:22.931759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile22.75
Q143
median61.3
Q376.74
95-th percentile111.744
Maximum8284
Range8283.9
Interquartile range (IQR)33.74

Descriptive statistics

Standard deviation60.48957233
Coefficient of variation (CV)0.9427774267
Kurtosis7123.291284
Mean64.16103167
Median Absolute Deviation (MAD)16.92
Skewness62.02338371
Sum4544333.39
Variance3658.988361
MonotonicityNot monotonic
2021-10-06T01:22:23.228423image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6584
 
< 0.1%
7079
 
< 0.1%
6068
 
< 0.1%
4067
 
< 0.1%
5064
 
< 0.1%
3064
 
< 0.1%
6762
 
< 0.1%
6459
 
< 0.1%
6859
 
< 0.1%
6259
 
< 0.1%
Other values (12450)70162
 
2.1%
(Missing)3258320
97.9%
ValueCountFrequency (%)
0.11
< 0.1%
0.351
< 0.1%
0.561
< 0.1%
0.61
< 0.1%
0.71
< 0.1%
0.751
< 0.1%
0.81
< 0.1%
0.851
< 0.1%
0.91
< 0.1%
0.941
< 0.1%
ValueCountFrequency (%)
82841
< 0.1%
67121
< 0.1%
29531
< 0.1%
2687.52
< 0.1%
1894.231
< 0.1%
1752.41
< 0.1%
1723.331
< 0.1%
1670.71
< 0.1%
1661.791
< 0.1%
13791
< 0.1%

lot3_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3292644
Missing (%)98.9%
Memory size25.4 MiB

lot3_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4932
Distinct (%)70.2%
Missing3322123
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean84.62386247
Minimum0.4
Maximum8284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:23.556006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile12.806
Q138.895
median61.775
Q388.4625
95-th percentile170.0885
Maximum8284
Range8283.6
Interquartile range (IQR)49.5675

Descriptive statistics

Standard deviation235.1677423
Coefficient of variation (CV)2.778976703
Kurtosis493.6550396
Mean84.62386247
Median Absolute Deviation (MAD)24.475
Skewness20.28587031
Sum594398.01
Variance55303.86702
MonotonicityNot monotonic
2021-10-06T01:22:23.871080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.2934
 
< 0.1%
12.531
 
< 0.1%
1216
 
< 0.1%
7014
 
< 0.1%
1514
 
< 0.1%
21.5713
 
< 0.1%
57.813
 
< 0.1%
40.0213
 
< 0.1%
1012
 
< 0.1%
27.5812
 
< 0.1%
Other values (4922)6852
 
0.2%
(Missing)3322123
99.8%
ValueCountFrequency (%)
0.41
 
< 0.1%
0.51
 
< 0.1%
0.531
 
< 0.1%
0.871
 
< 0.1%
14
< 0.1%
1.151
 
< 0.1%
1.181
 
< 0.1%
1.252
< 0.1%
1.291
 
< 0.1%
1.32
< 0.1%
ValueCountFrequency (%)
82841
 
< 0.1%
68001
 
< 0.1%
45031
 
< 0.1%
4331.411
< 0.1%
2692.32
 
< 0.1%
2159.871
 
< 0.1%
21041
 
< 0.1%
1894.231
 
< 0.1%
12091
 
< 0.1%
1163.341
 
< 0.1%

lot4_numero
Real number (ℝ≥0)

MISSING
SKEWED

Distinct792
Distinct (%)6.2%
Missing3316424
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean157.1274071
Minimum2
Maximum161313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:24.207156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median24
Q369
95-th percentile372
Maximum161313
Range161311
Interquartile range (IQR)61

Descriptive statistics

Standard deviation2171.604573
Coefficient of variation (CV)13.82066066
Kurtosis3816.304465
Mean157.1274071
Median Absolute Deviation (MAD)19
Skewness58.24379501
Sum1999132
Variance4715866.42
MonotonicityNot monotonic
2021-10-06T01:22:24.520736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9778
 
< 0.1%
8730
 
< 0.1%
7705
 
< 0.1%
6661
 
< 0.1%
4649
 
< 0.1%
5586
 
< 0.1%
3343
 
< 0.1%
13221
 
< 0.1%
2220
 
< 0.1%
15153
 
< 0.1%
Other values (782)7677
 
0.2%
(Missing)3316424
99.6%
ValueCountFrequency (%)
2220
 
< 0.1%
3343
< 0.1%
4649
< 0.1%
5586
< 0.1%
6661
< 0.1%
7705
< 0.1%
8730
< 0.1%
9778
< 0.1%
116
 
< 0.1%
12119
 
< 0.1%
ValueCountFrequency (%)
1613131
< 0.1%
1313131
< 0.1%
1020931
< 0.1%
320041
< 0.1%
200841
< 0.1%
160941
< 0.1%
132281
< 0.1%
130741
< 0.1%
130371
< 0.1%
130081
< 0.1%

lot4_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1564
Distinct (%)83.6%
Missing3327276
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean115.9817157
Minimum0.35
Maximum4331.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:24.861815image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.35
5-th percentile11.025
Q136.42
median67
Q3106.74
95-th percentile234.53
Maximum4331.4
Range4331.05
Interquartile range (IQR)70.32

Descriptive statistics

Standard deviation351.8314963
Coefficient of variation (CV)3.033508293
Kurtosis122.3391223
Mean115.9817157
Median Absolute Deviation (MAD)34.34
Skewness10.72466346
Sum217001.79
Variance123785.4018
MonotonicityNot monotonic
2021-10-06T01:22:25.142877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.514
 
< 0.1%
4331.411
 
< 0.1%
57.3310
 
< 0.1%
52.8510
 
< 0.1%
29.59
 
< 0.1%
70.58
 
< 0.1%
127
 
< 0.1%
106
 
< 0.1%
405
 
< 0.1%
29.135
 
< 0.1%
Other values (1554)1786
 
0.1%
(Missing)3327276
99.9%
ValueCountFrequency (%)
0.351
 
< 0.1%
0.41
 
< 0.1%
0.81
 
< 0.1%
0.891
 
< 0.1%
0.921
 
< 0.1%
11
 
< 0.1%
1.252
< 0.1%
1.481
 
< 0.1%
1.82
< 0.1%
24
< 0.1%
ValueCountFrequency (%)
4331.411
< 0.1%
2687.22
 
< 0.1%
1750.51
 
< 0.1%
1681.91
 
< 0.1%
1348.011
 
< 0.1%
1251.441
 
< 0.1%
927.71
 
< 0.1%
894.641
 
< 0.1%
881.181
 
< 0.1%
776.721
 
< 0.1%

lot5_numero
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3323135
Missing (%)99.8%
Memory size25.4 MiB

lot5_surface_carrez
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct647
Distinct (%)84.4%
Missing3328380
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean121.0577705
Minimum0.6
Maximum8188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:25.469139image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile10.365
Q129.615
median62.14
Q3113.645
95-th percentile302.286
Maximum8188
Range8187.4
Interquartile range (IQR)84.03

Descriptive statistics

Standard deviation399.6793349
Coefficient of variation (CV)3.301558695
Kurtosis244.9651218
Mean121.0577705
Median Absolute Deviation (MAD)38.94
Skewness14.16510403
Sum92851.31
Variance159743.5707
MonotonicityNot monotonic
2021-10-06T01:22:25.748200image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.3712
 
< 0.1%
117.910
 
< 0.1%
29.510
 
< 0.1%
90.198
 
< 0.1%
16.717
 
< 0.1%
12.55
 
< 0.1%
54.945
 
< 0.1%
114
 
< 0.1%
124
 
< 0.1%
664
 
< 0.1%
Other values (637)698
 
< 0.1%
(Missing)3328380
> 99.9%
ValueCountFrequency (%)
0.61
< 0.1%
0.931
< 0.1%
12
< 0.1%
1.331
< 0.1%
1.891
< 0.1%
2.751
< 0.1%
31
< 0.1%
3.21
< 0.1%
3.41
< 0.1%
3.521
< 0.1%
ValueCountFrequency (%)
81881
< 0.1%
4331.41
< 0.1%
3418.581
< 0.1%
2837.221
< 0.1%
2683.52
< 0.1%
997.881
< 0.1%
9241
< 0.1%
625.951
< 0.1%
614.691
< 0.1%
5911
< 0.1%

nombre_lots
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3985618538
Minimum0
Maximum330
Zeros2294729
Zeros (%)68.9%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:26.069781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum330
Range330
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8362805707
Coefficient of variation (CV)2.098245386
Kurtosis11491.95161
Mean0.3985618538
Median Absolute Deviation (MAD)0
Skewness46.98893099
Sum1326871
Variance0.6993651929
MonotonicityNot monotonic
2021-10-06T01:22:26.373711image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02294729
68.9%
1817196
 
24.5%
2180719
 
5.4%
323780
 
0.7%
46711
 
0.2%
52346
 
0.1%
61259
 
< 0.1%
7668
 
< 0.1%
8438
 
< 0.1%
9284
 
< 0.1%
Other values (72)1017
 
< 0.1%
ValueCountFrequency (%)
02294729
68.9%
1817196
 
24.5%
2180719
 
5.4%
323780
 
0.7%
46711
 
0.2%
52346
 
0.1%
61259
 
< 0.1%
7668
 
< 0.1%
8438
 
< 0.1%
9284
 
< 0.1%
ValueCountFrequency (%)
3301
< 0.1%
2231
< 0.1%
1981
< 0.1%
1361
< 0.1%
1311
< 0.1%
1211
< 0.1%
1201
< 0.1%
1191
< 0.1%
1161
< 0.1%
1121
< 0.1%

code_type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1512780
Missing (%)45.4%
Memory size25.4 MiB
1.0
648413 
2.0
585969 
3.0
450430 
4.0
131555 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0648413
19.5%
2.0585969
 
17.6%
3.0450430
 
13.5%
4.0131555
 
4.0%
(Missing)1512780
45.4%

Length

2021-10-06T01:22:26.706325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:22:26.891873image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0648413
35.7%
2.0585969
32.3%
3.0450430
24.8%
4.0131555
 
7.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_local
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing1512780
Missing (%)45.4%
Memory size25.4 MiB
Maison
648413 
Appartement
585969 
Dépendance
450430 
Local industriel. commercial ou assimilé
131555 

Length

Max length40
Median length10
Mean length11.06749737
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAppartement
2nd rowDépendance
3rd rowMaison
4th rowMaison
5th rowMaison

Common Values

ValueCountFrequency (%)
Maison648413
19.5%
Appartement585969
 
17.6%
Dépendance450430
 
13.5%
Local industriel. commercial ou assimilé131555
 
4.0%
(Missing)1512780
45.4%

Length

2021-10-06T01:22:27.153932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-06T01:22:27.532020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
maison648413
27.7%
appartement585969
25.0%
dépendance450430
19.2%
assimilé131555
 
5.6%
ou131555
 
5.6%
commercial131555
 
5.6%
industriel131555
 
5.6%
local131555
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_reelle_bati
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct4602
Distinct (%)0.3%
Missing1970649
Missing (%)59.2%
Infinite0
Infinite (%)0.0%
Mean115.3422611
Minimum1
Maximum277814
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:27.877116image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q149
median75
Q3104
95-th percentile190
Maximum277814
Range277813
Interquartile range (IQR)55

Descriptive statistics

Standard deviation806.5258823
Coefficient of variation (CV)6.992457706
Kurtosis32505.40326
Mean115.3422611
Median Absolute Deviation (MAD)27
Skewness134.4592681
Sum156692231
Variance650483.9989
MonotonicityNot monotonic
2021-10-06T01:22:28.199188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8026530
 
0.8%
6025286
 
0.8%
9023641
 
0.7%
7023502
 
0.7%
10020189
 
0.6%
5020006
 
0.6%
4019018
 
0.6%
6518973
 
0.6%
7516445
 
0.5%
4516022
 
0.5%
Other values (4592)1148886
34.5%
(Missing)1970649
59.2%
ValueCountFrequency (%)
1363
 
< 0.1%
2311
 
< 0.1%
3372
 
< 0.1%
4195
 
< 0.1%
5307
 
< 0.1%
6363
 
< 0.1%
7552
 
< 0.1%
8933
 
< 0.1%
91172
 
< 0.1%
103439
0.1%
ValueCountFrequency (%)
2778142
< 0.1%
2152901
< 0.1%
2071341
< 0.1%
1340001
< 0.1%
1320491
< 0.1%
1230001
< 0.1%
1128961
< 0.1%
1066831
< 0.1%
1009961
< 0.1%
998501
< 0.1%

nombre_pieces_principales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct46
Distinct (%)< 0.1%
Missing1515599
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean2.353666956
Minimum0
Maximum90
Zeros581298
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:28.525263image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum90
Range90
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.082816505
Coefficient of variation (CV)0.8849240545
Kurtosis3.945167896
Mean2.353666956
Median Absolute Deviation (MAD)2
Skewness0.653470421
Sum4268488
Variance4.338124595
MonotonicityNot monotonic
2021-10-06T01:22:28.840332image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0581298
 
17.5%
4300776
 
9.0%
3299373
 
9.0%
2220792
 
6.6%
5174948
 
5.3%
1127847
 
3.8%
668048
 
2.0%
724857
 
0.7%
88814
 
0.3%
93389
 
0.1%
Other values (36)3406
 
0.1%
(Missing)1515599
45.5%
ValueCountFrequency (%)
0581298
17.5%
1127847
 
3.8%
2220792
 
6.6%
3299373
9.0%
4300776
9.0%
5174948
 
5.3%
668048
 
2.0%
724857
 
0.7%
88814
 
0.3%
93389
 
0.1%
ValueCountFrequency (%)
901
< 0.1%
631
< 0.1%
551
< 0.1%
521
< 0.1%
501
< 0.1%
481
< 0.1%
411
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%

code_nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1050173
Missing (%)31.5%
Memory size25.4 MiB
S
1052935 
T
349321 
P
180010 
AB
131207 
J
115605 
Other values (22)
449896 

Length

Max length2
Median length1
Mean length1.204297636
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowAG
3rd rowAG
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S1052935
31.6%
T349321
 
10.5%
P180010
 
5.4%
AB131207
 
3.9%
J115605
 
3.5%
BT97939
 
2.9%
L93441
 
2.8%
AG78818
 
2.4%
VI42017
 
1.3%
BR34449
 
1.0%
Other values (17)103232
 
3.1%
(Missing)1050173
31.5%

Length

2021-10-06T01:22:29.185410image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s1052935
46.2%
t349321
 
15.3%
p180010
 
7.9%
ab131207
 
5.8%
j115605
 
5.1%
bt97939
 
4.3%
l93441
 
4.1%
ag78818
 
3.5%
vi42017
 
1.8%
br34449
 
1.5%
Other values (17)103232
 
4.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)< 0.1%
Missing1050173
Missing (%)31.5%
Memory size25.4 MiB
sols
1052935 
terres
349321 
prés
180010 
terrains a bâtir
131207 
jardins
115605 
Other values (22)
449896 

Length

Max length19
Median length4
Mean length6.717378083
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsols
2nd rowterrains d'agrément
3rd rowterrains d'agrément
4th rowsols
5th rowsols

Common Values

ValueCountFrequency (%)
sols1052935
31.6%
terres349321
 
10.5%
prés180010
 
5.4%
terrains a bâtir131207
 
3.9%
jardins115605
 
3.5%
taillis simples97939
 
2.9%
landes93441
 
2.8%
terrains d'agrément78818
 
2.4%
vignes42017
 
1.3%
futaies résineuses34449
 
1.0%
Other values (17)103232
 
3.1%
(Missing)1050173
31.5%

Length

2021-10-06T01:22:29.479493image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sols1052935
37.6%
terres349428
 
12.5%
terrains210025
 
7.5%
prés182504
 
6.5%
a131207
 
4.7%
bâtir131207
 
4.7%
jardins115605
 
4.1%
taillis114762
 
4.1%
simples97939
 
3.5%
landes93800
 
3.4%
Other values (24)318731
 
11.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

code_nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct125
Distinct (%)0.1%
Missing3175092
Missing (%)95.4%
Memory size25.4 MiB
POTAG
32746 
PATUR
16277 
PIN
12625 
PARC
12578 
FRICH
9834 
Other values (120)
69995 

Length

Max length5
Median length5
Mean length4.483165103
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowJARD
2nd rowJARD
3rd rowETANG
4th rowPARC
5th rowPATUR

Common Values

ValueCountFrequency (%)
POTAG32746
 
1.0%
PATUR16277
 
0.5%
PIN12625
 
0.4%
PARC12578
 
0.4%
FRICH9834
 
0.3%
VAOC7598
 
0.2%
IMM5027
 
0.2%
CHAT4875
 
0.1%
PACAG3476
 
0.1%
MARAI3451
 
0.1%
Other values (115)45568
 
1.4%
(Missing)3175092
95.4%

Length

2021-10-06T01:22:29.799565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
potag32746
21.3%
patur16277
 
10.6%
pin12625
 
8.2%
parc12578
 
8.2%
frich9834
 
6.4%
vaoc7598
 
4.9%
imm5027
 
3.3%
chat4875
 
3.2%
pacag3476
 
2.3%
marai3451
 
2.2%
Other values (115)45568
29.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nature_culture_speciale
Categorical

HIGH CARDINALITY
MISSING

Distinct125
Distinct (%)0.1%
Missing3175092
Missing (%)95.4%
Memory size25.4 MiB
Jardin potager
32746 
Pâture plantée
16277 
Pins
12625 
Parc
12578 
Friche
9834 
Other values (120)
69995 

Length

Max length38
Median length14
Mean length12.7497582
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowJardin d'agrément
2nd rowJardin d'agrément
3rd rowEtangs
4th rowParc
5th rowPâture plantée

Common Values

ValueCountFrequency (%)
Jardin potager32746
 
1.0%
Pâture plantée16277
 
0.5%
Pins12625
 
0.4%
Parc12578
 
0.4%
Friche9834
 
0.3%
Vins d'appellation d'origine contrôlée7598
 
0.2%
Dépendances d'ensemble immobilier5027
 
0.2%
Châtaigneraie4875
 
0.1%
Pacage3476
 
0.1%
Pré marais3451
 
0.1%
Other values (115)45568
 
1.4%
(Missing)3175092
95.4%

Length

2021-10-06T01:22:30.107894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jardin34246
 
12.1%
potager32746
 
11.6%
pâture16277
 
5.8%
plantée16277
 
5.8%
pins12625
 
4.5%
parc12584
 
4.5%
friche9834
 
3.5%
ou8488
 
3.0%
vins7818
 
2.8%
d'origine7598
 
2.7%
Other values (159)123872
43.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface_terrain
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct46290
Distinct (%)2.0%
Missing1050235
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean3113.500546
Minimum1
Maximum4625500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 MiB
2021-10-06T01:22:30.388958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1236
median627
Q31933
95-th percentile12491.45
Maximum4625500
Range4625499
Interquartile range (IQR)1697

Descriptive statistics

Standard deviation14470.70868
Coefficient of variation (CV)4.647729612
Kurtosis17242.01383
Mean3113.500546
Median Absolute Deviation (MAD)503
Skewness82.35419667
Sum7095393757
Variance209401409.8
MonotonicityNot monotonic
2021-10-06T01:22:30.713544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50043063
 
1.3%
100020255
 
0.6%
6006636
 
0.2%
8006628
 
0.2%
125790
 
0.2%
4005397
 
0.2%
7005340
 
0.2%
135319
 
0.2%
1005150
 
0.2%
3005143
 
0.2%
Other values (46280)2170191
65.2%
(Missing)1050235
31.5%
ValueCountFrequency (%)
14712
0.1%
24071
0.1%
33446
0.1%
43776
0.1%
53895
0.1%
63726
0.1%
73656
0.1%
83609
0.1%
93334
0.1%
104530
0.1%
ValueCountFrequency (%)
46255001
 
< 0.1%
39890551
 
< 0.1%
38058801
 
< 0.1%
35918001
 
< 0.1%
30327711
 
< 0.1%
29600003
< 0.1%
28893001
 
< 0.1%
27455001
 
< 0.1%
26365761
 
< 0.1%
25857001
 
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1790095
Distinct (%)55.0%
Missing72660
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean2.234953837
Minimum-63.146385
Maximum55.826361
Zeros0
Zeros (%)0.0%
Negative751121
Negative (%)22.6%
Memory size25.4 MiB
2021-10-06T01:22:31.046129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-63.146385
5-th percentile-2.2577305
Q10.205196
median2.336341
Q34.446119
95-th percentile6.5915134
Maximum55.826361
Range118.972746
Interquartile range (IQR)4.240923

Descriptive statistics

Standard deviation6.365984938
Coefficient of variation (CV)2.84837424
Kurtosis66.46723583
Mean2.234953837
Median Absolute Deviation (MAD)2.124593
Skewness-1.624439272
Sum7278098.116
Variance40.52576423
MonotonicityNot monotonic
2021-10-06T01:22:31.396229image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5131431158
 
< 0.1%
2.3177851052
 
< 0.1%
2.4551771004
 
< 0.1%
2.232054834
 
< 0.1%
2.201739764
 
< 0.1%
-1.213741714
 
< 0.1%
2.48249677
 
< 0.1%
4.410593652
 
< 0.1%
-1.213963613
 
< 0.1%
1.713259598
 
< 0.1%
Other values (1790085)3248421
97.6%
(Missing)72660
 
2.2%
ValueCountFrequency (%)
-63.1463856
< 0.1%
-63.1453123
 
< 0.1%
-63.1452868
< 0.1%
-63.1440023
 
< 0.1%
-63.1438452
 
< 0.1%
-63.1401414
< 0.1%
-63.1400952
 
< 0.1%
-63.1381251
 
< 0.1%
-63.1347881
 
< 0.1%
-63.1317142
 
< 0.1%
ValueCountFrequency (%)
55.8263612
< 0.1%
55.8258371
< 0.1%
55.8251791
< 0.1%
55.8242851
< 0.1%
55.8241921
< 0.1%
55.8233851
< 0.1%
55.8232871
< 0.1%
55.8228731
< 0.1%
55.8226081
< 0.1%
55.8213682
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct1731438
Distinct (%)53.2%
Missing72660
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean46.13576713
Minimum-21.386772
Maximum51.082118
Zeros0
Zeros (%)0.0%
Negative16431
Negative (%)0.5%
Memory size25.4 MiB
2021-10-06T01:22:31.832287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-21.386772
5-th percentile43.2150312
Q144.7221355
median46.725688
Q348.6938485
95-th percentile49.884219
Maximum51.082118
Range72.46889
Interquartile range (IQR)3.971713

Descriptive statistics

Standard deviation5.771072897
Coefficient of variation (CV)0.1250889116
Kurtosis95.67778275
Mean46.13576713
Median Absolute Deviation (MAD)1.976642
Skewness-8.896969398
Sum150240525.9
Variance33.30528238
MonotonicityNot monotonic
2021-10-06T01:22:32.179368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.6707491159
 
< 0.1%
48.635691051
 
< 0.1%
49.0353961004
 
< 0.1%
48.945538836
 
< 0.1%
48.944566764
 
< 0.1%
44.839384715
 
< 0.1%
48.903144677
 
< 0.1%
45.417475652
 
< 0.1%
44.848046613
 
< 0.1%
48.974742600
 
< 0.1%
Other values (1731428)3248416
97.6%
(Missing)72660
 
2.2%
ValueCountFrequency (%)
-21.3867725
< 0.1%
-21.3859521
 
< 0.1%
-21.3853892
 
< 0.1%
-21.3849994
< 0.1%
-21.3848061
 
< 0.1%
-21.3846447
< 0.1%
-21.3841081
 
< 0.1%
-21.3837992
 
< 0.1%
-21.3836151
 
< 0.1%
-21.383561
 
< 0.1%
ValueCountFrequency (%)
51.0821186
< 0.1%
51.0820452
 
< 0.1%
51.0819475
< 0.1%
51.0817656
< 0.1%
51.081712
 
< 0.1%
51.0816313
 
< 0.1%
51.0815768
< 0.1%
51.0813751
 
< 0.1%
51.0809422
 
< 0.1%
51.0807652
 
< 0.1%

Interactions

2021-10-06T01:20:00.714358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:48.449379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:12.482091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:39.121888image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:00.120762image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:23.921028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:30.531009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:38.876847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:45.482193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:51.546305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:58.090043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:04.109537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:13.596559image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:36.601002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:53.947326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:14.219497image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:34.506420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:20:03.406004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:51.045966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:15.086884image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:40.832302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:02.841275image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:24.301307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:31.127143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:39.278445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:45.874281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:51.945396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:58.459129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:04.515629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:16.003635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:38.088849image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:55.657266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:16.272189image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:37.201394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:20:05.265839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:52.729201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:16.892060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:42.723813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:04.671861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:24.691907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:31.709276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:39.700049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:46.256367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:52.344486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:58.841221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:04.898224image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:17.644428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:39.624059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:57.316527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:17.419075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:38.975742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:20:07.979243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:54.990211image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:19.632081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:44.650242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:07.467610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:25.112001image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:32.279405image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:40.089660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:46.619451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:52.741098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:59.178300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:05.245302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:20.158832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:41.114101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:59.046061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:19.437202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:41.692951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:20:08.393718image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:55.410586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:20.093209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:45.069650image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:07.881300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:25.455356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:32.639294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:40.443170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:46.971036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:53.150171image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:59.522376image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:05.631635image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:20.602894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:41.518720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:59.474180image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:19.864807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:42.130050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:20:09.037390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:15:55.957656image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:20.696783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:16:45.658820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:08.452935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:25.811438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:33.190418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:40.811125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:47.284499image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:53.465930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:17:59.856962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:05.979734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:21.162018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:18:42.141858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-10-06T01:19:31.656186image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-10-06T01:19:57.962678image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-06T01:22:34.172328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-06T01:22:34.921520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-06T01:22:35.370127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-06T01:20:32.587693image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-06T01:20:51.750828image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-06T01:21:43.847644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-06T01:21:56.946234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
02018-12018-01-031Vente109000.013.0NaNRUE GEN LOGEROT16601000.01053Bourg-en-Bresse1NaNNaN01053000AN0073NaNNaN1NaN31NaNNaNNaNNaNNaNNaNNaN22.0Appartement73.04.0NaNNaNNaNNaNNaN5.22046346.200053
12018-12018-01-031Vente109000.013.0NaNRUE GEN LOGEROT16601000.01053Bourg-en-Bresse1NaNNaN01053000AN0073NaNNaN13NaNNaNNaNNaNNaNNaNNaNNaNNaN13.0DépendanceNaN0.0NaNNaNNaNNaNNaN5.22046346.200053
22018-22018-01-041Vente239300.04.0NaNRUE DE LA BARMETTE00251250.01095Nivigne et Suran1NaNNaN01095000AH0186NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison163.04.0SsolsNaNNaN949.05.40802546.255574
32018-22018-01-041Vente239300.04.0NaNRUE DE LA BARMETTE00251250.01095Nivigne et Suran1NaNNaN01095000AH0186NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison163.04.0AGterrains d'agrémentJARDJardin d'agrément420.05.40802546.255574
42018-22018-01-041Vente239300.04.0NaNRUE DE LA BARMETTE00251250.01095Nivigne et Suran1NaNNaN01095000AH0186NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison51.02.0AGterrains d'agrémentJARDJardin d'agrément420.05.40802546.255574
52018-22018-01-041Vente239300.04.0NaNRUE DE LA BARMETTE00251250.01095Nivigne et Suran1NaNNaN01095000AH0186NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN01.0Maison51.02.0SsolsNaNNaN949.05.40802546.255574
62018-32018-01-041Vente90000.0NaNNaNLE DRUILLETB0341380.01343Saint-Cyr-sur-Menthon1NaNNaN01343000ZR0359NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNSsolsNaNNaN278.04.94170246.263993
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92018-42018-01-102Vente3150.0NaNNaNPONT D AINB0771160.01304Pont-d'Ain1NaNNaN01304000AM0461NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNSsolsNaNNaN126.05.34423846.050637

Last rows

id_mutationdate_mutationnumero_dispositionnature_mutationvaleur_fonciereadresse_numeroadresse_suffixeadresse_nom_voieadresse_code_voiecode_postalcode_communenom_communecode_departementancien_code_communeancien_nom_communeid_parcelleancien_id_parcellenumero_volumelot1_numerolot1_surface_carrezlot2_numerolot2_surface_carrezlot3_numerolot3_surface_carrezlot4_numerolot4_surface_carrezlot5_numerolot5_surface_carreznombre_lotscode_type_localtype_localsurface_reelle_batinombre_pieces_principalescode_nature_culturenature_culturecode_nature_culture_specialenature_culture_specialesurface_terrainlongitudelatitude
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33291382018-14325912018-12-311Vente1524.499.0NaNPL DES VOSGES991775004.075104Paris 4e Arrondissement75NaNNaN75104000AO0007NaNNaN103NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement20.02.0NaNNaNNaNNaNNaN2.36390548.855175
33291392018-14325922018-12-271Vente1800.009.0NaNPL DES VOSGES991775004.075104Paris 4e Arrondissement75NaNNaN75104000AO0007NaNNaN109NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement20.02.0NaNNaNNaNNaNNaN2.36390548.855175
33291402018-14325932018-12-281Vente405000.0013.0NaNRUE BEAUTREILLIS079775004.075104Paris 4e Arrondissement75NaNNaN75104000AQ0016NaNNaN1633.8727NaNNaNNaNNaNNaNNaNNaN22.0Appartement34.01.0NaNNaNNaNNaNNaN2.36363748.853114
33291412018-14325942018-12-261Vente220000.0014.0NaNRUE DES LIONS SAINT PAUL570275004.075104Paris 4e Arrondissement75NaNNaN75104000AQ0127NaNNaN126NaN26NaNNaNNaNNaNNaNNaNNaN22.0Appartement29.01.0NaNNaNNaNNaNNaN2.36175648.852988
33291422018-14325952018-12-281Vente1192307.001.0NaNPAS CHOISEUL201275002.075102Paris 2e Arrondissement75NaNNaN75102000AD0118NaNNaN43120.5444NaNNaNNaNNaNNaNNaNNaN22.0Appartement150.04.0NaNNaNNaNNaNNaN2.33513448.867251
33291432018-14325962018-12-031Vente383000.0012.0NaNRUE POISSONNIERE756175002.075102Paris 2e Arrondissement75NaNNaN75102000AO0085NaNNaN934.78NaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement34.01.0NaNNaNNaNNaNNaN2.34797848.869023
33291442018-14325972018-12-281Vente746000.0018.0NaNRUE STE CROIX BRETONNERIE854875004.075104Paris 4e Arrondissement75NaNNaN75104000AH0053NaNNaN863.46NaNNaNNaNNaNNaNNaNNaNNaN14.0Local industriel. commercial ou assimilé60.00.0NaNNaNNaNNaNNaN2.35656748.858258
33291452018-14325982018-12-131Adjudication645000.009.0NaNBD MORLAND655975004.075104Paris 4e Arrondissement75NaNNaN75104000AS0074NaNNaN101NaN122NaNNaNNaNNaNNaNNaNNaN22.0Appartement54.02.0NaNNaNNaNNaNNaN2.36456448.848321
33291462018-14325992018-10-171Vente45000.00273.0NaNRUE SAINT-DENIS852575002.075102Paris 2e Arrondissement75NaNNaN75102000AP0117NaNNaN57NaNNaNNaNNaNNaNNaNNaNNaNNaN12.0Appartement11.01.0NaNNaNNaNNaNNaN2.35165548.868705